Determine Smoking Status Research Articles

Efficacy of nutritional supplement (Haras) on carbon monoxide levels in smokers and non-smokers: An observational study.

Carbon monoxide (CO) concentrations in exhaled air may impart a quick, non-invasive method to determine smoking status. Haras is a nutraceutical medication, which is slowly gaining recognition for its antioxidant and anti-inflammatory activities. The effectiveness of the Haras therapy in smokers and non-smokers will be assessed by evaluating breath CO levels. The study included 101 test subjects with 76 subjects of smokers and 25 subjects of non-smokers. Both the test groups were given 10 mL of Haras juice in divided doses per day for 30 days. The CO levels were evaluated using a breath analyser before drug trial and then on the 8th, 15th, 22nd and after the conclusion of the drug trial. The Wilcoxon signed-rank test was used to compare the CO and carboxyhemoglobin levels among smokers and non-smokers. Smokers had higher mean percent carboxyhemoglobin and mean parts per million CO values than non-smokers, and the difference between the two was shown to be statistically significant (P < 0.001). It was also found to be statistically significant from the first day to the eighth day, the first day to the 15th day, the first day to the 20th second day, first day to the 30th day (P < 0.001). Haras can be used effectively as an alternative supportive treatment for the diminution of CO levels in smokers and non-smokers.

Smoking status trajectories, intergenerational socioeconomic mobility and depression: Preliminary results from 107,734 French adults (18 to 75 years) of the CONSTANCES cohort

IntroductionCigarette smoking prevalence is significantly higher among people with mental health problems than among the general population. Smoking accounts for much of the reduction in life expectancy associated with mental illness, why the high co-occurrence of smoking and mental health illness is a major public health concern. Persons belonging to socioeconomical disadvantaged groups have higher risk of mental health conditions and also higher smoking rates.ObjectivesIn this study we aim to examine smoking trajectories among adult smokers between 2012 and 2020. Furthermore, we aim to investigate differences in smoking trajectories by adult depression by taking into consideration participants intergenerational socioeconomic mobility (ISEM).MethodsAnalyses were based on data from CONSTANCES, a French general population cohort conducted from 2012 to 2020. In total were 107,734 participants included after exclusion of never smokers. Depression was measured by the CES-D scale, and depression was classified with a score ≥16. ISEM is based on childhood (maternal and parental occupational grade) and adult socioeconomic position (SEP), and low ISEM includes those with low SEP as child and adult and high ISEM those with consistent high SEP. Group-based trajectories modelling (GBTM) was used to determine smoking status trajectories. To address the association between ISEM and smoking trajectory class we used multinomial logistic regression with former smokers as reference class adjusted for depression, household income, sex and age.ResultsWe identified five smoking trajectories 1) Former smokers (56.6%), 2) Long-term smokers (26.4%), 3) Intermediate smokers (3.3%), 4) Early quitters (5.0%) and 5) Late quitters (8.7%). Preliminary results from multinomial logistic regression showed that persons with low ISEM had higher odds of depression (OR [95%CI]=1.91 [1.77;2.06]) than those with high ISEM. Participants with low ISEM had higher odds of being long-term smoking than former smokers compared to those with high ISEM (ORa [95%CI]=1.55 [1.43;1.67]). Furthermore, those with low ISEM had lower odds of being in any of the other smoking trajectory groups vs. former smokers compared to those with high ISEM (ORa [95%CI]=0.82 [0.69;0.97]) for intermediate smokers, ORa [95%CI]=0.75 [0.66;0.85]) for early quitters, and ORa [95%CI]=0.78 [0.70;0.87]) for late quitters).ConclusionsPreliminary results showed an association between ISEM and smoking trajectories in our study. Persons with low ISEM are more likely to be long-term smokers. Future analysis should consider the effect of depression as a mediating factor on the association between ISEM and smoking trajectories.Disclosure of InterestNone Declared

Pyrosequencing: Current forensic methodology and future applications-a review.

The recent development of small, single-amplicon-based benchtop systems for pyrosequencing has opened up a host of novel procedures for applications in forensic science. Pyrosequencing is a sequencing by synthesis technique, based on chemiluminescent inorganic pyrophosphate detection. This review explains the pyrosequencing workflow and illustrates the step-by-step chemistry, followed by a description of the assay design and factors to keep in mind for an exemplary assay. Existing and potential forensic applications are highlighted using this technology. Current applications include identifying species, identifying bodily fluids, and determining smoking status. We also review progress in potential applications for the future, including research on distinguishing monozygotic twins, detecting alcohol and drug abuse, and other phenotypic characteristics such as diet and body mass index. Overall, the versatility of the pyrosequencing technologies renders it a useful tool in forensic genomics.

A Systematic Review and Scoping Analysis of Smoking Cessation after a Urological Cancer Diagnosis

Smoking cessation after a urologic cancer diagnosis significantly benefits patients. It is not well known how often patients quit after diagnosis or how urologists intervene to support patients' smoking cessation efforts. To examine rates of smoking cessation after diagnosis among patients with urologic cancers and to assess how often patients are given advice and support to quit smoking in the urology setting. Following the PRISMA guidelines, a systematic review was conducted of the available studies on smoking cessation after a urologic cancer diagnosis during April 2020 by a trained medical librarian using the MEDLINE, PsycInfo, Embase, and Cochrane CENTRAL databases. Studies were included based on three independent reviews and if they met a priori inclusion/exclusion criteria. In total, 2568 records were identified, 31 of which were included for final analysis. Four studies (patient n=587) reported outcomes related to the prospective implementation of a smoking cessation program with patient-level quit rates ranging from 3.2% to 47.3%. Twenty one studies reported outcomes of passive (no directed, active intervention) smoking cessation after the diagnosis of a urologic cancer (patient n=3669) with widely varying quit rates. In general, the quality of included studies was poor. There was no standardization of the measurement or timing of outcomes and few studies included validated survey instruments or biochemical confirmation of cessation. Seventeen studies included data on whether patients received advice to quit smoking after diagnosis. The proportion of patients in each study who were told to quit ranged from 2.8-78.3%. There are few smoking cessation interventions that have been prospectively implemented and reported in the urology literature and studies on quit rates after diagnosis are limited. The paucity of quality data and lack of smoking cessation interventions being used in routine urologic oncology care underscores the need for more rigorous study and implementation of evidence-based practices in this area.

Tobacco smoking and risk of all-cause mortality in Indonesia.

Tobacco is well known as a risk factor for early morbidity and mortality worldwide. However, the relative risk of mortality and the effects of smoking vary among the countries. Indonesia, as one of the world’s largest market for smoking tobacco, is significantly affected by tobacco-related illness. Previous research has shown that smoking causes several diseases, including stroke, neoplasm and coronary heart disease. There has to date been no research on the hazard risk of smoking for all-cause mortality in Indonesia. This study aimed to identify the association between smoking and all-cause mortality rates in Indonesia. Information from a total of 3,353 respondents aged 40 years and older was collected in this study. The data were taken from the Indonesian Family Life Survey (IFLS) Wave 4 (2007) to collect personal information and determine smoking status and from Wave 5 (2015) to collect information about deaths. Current smokers make up 40.3% of Indonesia’s population. Current smokers were more likely to have a higher risk of all-cause death (hazard ratio = 1.48, 95% confidence interval = 1.11 to 1.98) than non-current smokers. The number of smokers in Indonesia remains high and is expected to increase gradually every year. A firm government policy is needed to reduce the number of smokers in Indonesia which would automatically reduce the health problem of smoking-related illness in the future.

Factors associated with smoking cessation in patients with coronary heart disease: a cohort analysis of the German subset of EuroAspire IV survey

BackgroundTobacco smoking is one of the most important risk factors of coronary heart disease (CHD). Hence, smoking cessation is considered pivotal in the prevention of CHD. The current study aimed to evaluate smoking cessation patterns and determine factors associated with smoking cessation in patients with established CHD.MethodsThe fourth European Survey of Cardiovascular Disease Prevention and Diabetes investigated quality of CHD care in 24 countries across Europe in 2012/13. In the German subset, smoking cessation patterns and clinical characteristics were repetitively assessed a) during index event due to CHD by medical record abstraction, b) as part of a face-to-face interview 6 to 36 months after the index event (i.e. baseline visit), and c) by telephone-based follow-up interview two years after the baseline visit. Logistic regression analysis was performed to search for factors determining smoking status at the time of the telephone interview.ResultsOut of 469 participants available for follow-up, 104 (22.2%) had been classified as current smokers at the index event. Of those, 65 patients (62.5%) had quit smoking at the time of the telephone interview, i.e., after a median observation period of 3.5 years (quartiles 3.0, 4.1). Depressed mood at baseline visit and higher education level were less prevalent amongst quitters vs non-quitters (17.2% vs 35.9%, p = 0.03 and 15.4% vs 33.3%, p = 0.03), cardiac rehabilitation programs were more frequently attended by quitters (83.1% vs 48.7%, p < 0.001), and there was a trend for a higher prevalence of diabetes at baseline visit in quitters (37.5% vs 20.5%, p = 0.07). In the final multivariable model, cardiac rehabilitation was associated with smoking cessation (OR 5.19; 95%CI 1.87 to 14.46; p = 0.002).DiscussionAttending a cardiac rehabilitation program after a cardiovascular event was associated with smoking cessation supporting its use as a platform for smoking cessation counseling and relapse prevention.

Smoking and smoking cessation among people with chronic obstructive pulmonary disease (COPD)

RATIONALE: Despite its importance as a leading risk factor for chronic obstructive pulmonary disease (COPD), there is a minimal amount of population-level information regarding smoking prevalence among people with COPD. OBJECTIVES The objectives were to describe trends in smoking prevalence, cessation counseling and success among persons with COPD in Ontario, Canada. METHODS We conducted a repeated cross-sectional study among individuals with physician-diagnosed COPD using a linked survey and health administrative data between 2005 and 2012. Survey respondents aged 40 and older were stratified on presence of physician-diagnosed COPD. Responses were used to determine smoking status and cessation success; health administrative data were used to determine smoking cessation counseling. MEASUREMENT and main results: In 2012, the age-sex standardized prevalence of smoking in the COPD population was 31.1% (95% CI 30.3–32.1), more than double the general population prevalence (12.2%, 95% CI 12.6–13.1), with no decrease over the study period. COPD smokers received cessation counseling twice as often as general population smokers (21.9%, 95% CI 20.4–23.5 vs 12.8%, 95% CI 12.1–13.5), but had similar quit rates (6.7%, 95% CI 5.9–7.6 vs 6.6%, 95% CI 6.2–7.0). CONCLUSION Smoking prevalence continues to be high in people with COPD, and higher rates of smoking cessation counseling do not result in greater quit rates. Smoking cessation strategies may need better tailoring to the COPD population to achieve smoking cessation success.

Smoking status classification by optical spectroscopy and partial least square regression

Smoking status of individuals is often revealed through self-reported data and interviews. The incidence of false reports severely impairs the proper assessment of the individuals’ health conditions and their risk to tobacco associated diseases, delays clinical intervention and treatment services. This paper presents the use of optical technique combined with partial least square (PLS) regression model in the classification of smoking status. The focus of this work is on light absorbance signals (by skin tissue) in the wavelength range of 520 – 600 nm; based on these data dendogram and PLS techniques are used to estimate the number of likely clusters within the considered dataset and to predict smoking status of individuals, respectively. The results from the processing of spectral information of smoking and nonsmoking populations revealed a high misclassification rate of 26.67 % using dendrogram method, but a considerably high accuracy of 90 % evaluated via leave one out cross validation was obtained using PLS component number 4. This study concluded that the spectral oscillation patterns and descending rates corresponded to nonsmoking and smoking individuals could be differentiated and specified using PLS technique in the determination of smoking status.

Applications of epigenetic methylation in body fluid identification, age determination and phenotyping

In this paper, four different applications are presented in forensic epigenetics using bisulfite modified PCR, HRM, and pyrosequencing. The first application involves the development of a multiplex amplification capable of simultaneous analysis of 4 different body fluid/cell types- blood, saliva, sperm, and vaginal epithelia. We have also developed a method for the detection of sperm high-resolution melt analysis (HRM). Two other applications involve a determination of suspect age and a biomarker for determining smoking status.

Epismoker: A Robust Classifier to Determine Smoking Status from DNA Methylation Data

Aim: Smoking strongly influences DNA methylation, with current and never smokers exhibiting different methylation profiles. Methods: To advance the practical applicability of the smoking-associated methylation signals, we used machine learning methodology to train a classifier for smoking status prediction. Results: We show the prediction performance of our classifier on three independent whole-blood datasets demonstrating its robustness and global applicability. Furthermore, we examine the reasons for biologically meaningful misclassifications through comprehensive phenotypic evaluation. Conclusion: The major contribution of our classifier is its global applicability without a need for users to determine a threshold value for each dataset to predict the smoking status. We provide an R package, EpiSmokEr (Epigenetic Smoking status Estimator), facilitating the use of our classifier to predict smoking status in future studies.

AHRR methylation predicts smoking status and smoking intensity in both saliva and blood DNA.

Many existing DNA repositories do not have robust characterizations of smoking, while for many currently ongoing studies, the advent of vaping has rendered traditional cotinine-based methods of determining smoking status unreliable. Previously, we have shown that methylation status at cg05575921 in whole blood DNA can reliably predict cigarette consumption. However, whether methylation status in saliva can be used similarly has yet to be established. Herein, we use DNA from 418 biochemically confirmed smokers or nonsmokers to compare and contrast the utility of cg05575921 in classifying and quantifying cigarette smoking. Using whole blood DNA, a model incorporating age, gender, and methylation status had a receiver operating characteristic (ROC) area under the curve (AUC) for predicting smoking status of 0.995 with a nonlinear demethylation response to smoking. Using saliva DNA, the ROC AUC for predicting smoking was 0.971 with the plot of the relationship of DNA methylation to daily cigarette consumption being very similar to that seen for whole blood DNA. The addition of information from another methylation marker designed to correct for cellular heterogeneity improved the AUC for saliva DNA to 0.981. Finally, in 31 subjects who reported quitting smoking 10 or more years previously, cg05575921 methylation was nonsignificantly different from controls. We conclude that DNA methylation status at cg05575921 in DNA from whole blood or saliva predicts smoking status and daily cigarette consumption. We suggest these epigenetic assessments for objectively ascertaining smoking status will find utility in research, clinical, and civil applications.

Detection of smoking status from retinal images; a Convolutional Neural Network study

Cardiovascular diseases are directly linked to smoking habits, which has both physiological and anatomical effects on the systemic and retinal circulations, and these changes can be detected with fundus photographs. Here, we aimed to 1- design a Convolutional Neural Network (CNN), using retinal photographs, to differentiate between smokers and non-smokers; and 2- use the attention maps to better understand the physiological changes that occur in the retina in smokers. 165,104 retinal images were obtained from a diabetes screening programme, labelled with self-reported “smoking” or “non-smoking” status. The images were pre-processed in one of two ways, either “contrast-enhanced” or “skeletonized”. Experiments were run on an Intel Xeon Gold 6128 CPU @ 3.40 GHz with 16 GB of RAM memory and a NVIDIA GeForce TiTan V VOLTA 12 GB, for 20 epochs. The dataset was split 80/20 for training and testing sets, respectively. The overall validation outcomes for the contrast-enhanced model were accuracy 88.88%, specificity 93.87%. In contrast, the outcomes of the skeletonized model were accuracy 63.63%, specificity 65.60%. The “attention maps” that were generated of the contrast-enhanced model highlighted the retinal vasculature, perivascular region and the fovea most prominently. We trained a customized CNN to accurately determine smoking status. The retinal vasculature, the perivascular region and the fovea appear to be important predictive features in the determination of smoking status. Despite a high degree of accuracy, the sensitivity of our CNN was low. Further research is required to establish whether the frequency, duration, and dosage (quantity) of smoking would improve the sensitivity of the CNN.

Effect of Cotinine-Verified Change in Smoking Status on Risk of Metachronous Colorectal Neoplasia After Polypectomy

Previous assessments of colorectal neoplasia (CRN) recurrence after polypectomy used self-report to determine smoking status. We evaluated the association between change in smoking status and metachronous CRN risk after polypectomy using cotinine level in urine to determine tobacco exposure. We performed a retrospective study of participants in the Kangbuk Samsung Health Study in Korea who underwent a screening colonoscopy examination and measurement of cotinine in urine samples. Our analysis included 4762 patients who had 1 or more adenomas detected in an index colonoscopy performed between January 2010 and December 2014, and underwent a surveillance colonoscopy, 6 or more months later, until December2017. Patients were classified into 4 groups based on the change in cotinine-verified smoking status from index to follow-up colonoscopy (mean interval, 3.2 ± 1.3 y), as follows: remained nonsmokers (n= 2962; group 1), smokers changed to nonsmokers (n= 600; group 2), nonsmokers changed to smokers (n= 138; group 3), and remained smokers (n= 1062; group 4). After adjustment for confounding factors, group 4 had a significantly higher risk of metachronous CRN than group 1 (hazard ratio [HR], 1.54; 95% CI, 1.36-1.73) and group 2 (HR, 1.63; 95% CI, 1.39-1.99). Group 4 also had a higher risk of metachronous advanced CRN than group 1 (HR, 2.84; 95% CI, 1.79-4.53) and group 2 (HR, 2.10; 95% CI, 1.13-3.89). Group 3 had a higher risk of metachronous CRN than group 1 (HR, 1.50; 95% CI, 1.14-1.97) and group 2 (HR, 1.62; 95% CI, 1.20-2.20). In a retrospective study of individuals with at least 1 adenoma, we found that cotinine-verified changes in smoking status between index and follow-up colonoscopy are associated with a risk of metachronous CRN. Helping patients quit smoking is important for effective prevention of colorectal cancer.

Biomarkers of Smoking - Which Cut-Off Values Should be Used?

Biomarkers of Smoking - Which Cut-Off Values Should be Used? Abstract. Verification of smoking status by means of biomarkers is important for treatment decisions of patients with smoking-related diseases. Cotinine is currently the best biomarker to document nicotine consumption. A low cost alternative method to determine smoking status is by measurement of carboxyhemoglobin (CO-Hb) in the exhaled breath. The main disadvantage of CO-Hb is the short half-life. The appropriate cut-off value for active nicotine consumption in Switzerland is 50 ng/ml or higher cotinine in the urine or 10 ng/ml and 12 ng/ml in serum and saliva, respectively. CO-Hb levels greater than 2 % indicate smoking with high probability, levels above 3 % with very high probability.

2,5-Dimethylfuran as a Validated Biomarker of Smoking Status.

Exposure biomarkers are required in tobacco use studies to accurately assess smoking status since self-reporting usually results in misclassification estimates. This study uses breath analysis and assesses some volatile organic compounds (VOCs) as potential biomarkers of tobacco smoke exposure. Forced-expiratory breath samples were obtained from 377 volunteers (174 smokers and 203 nonsmokers). Exhaled breath levels of different VOCs previously related to tobacco smoke were evaluated. The toluene-to-benzene ratio was evaluated as this ratio has been found to be different in atmospheric samples and tobacco smoke emissions. Finally, breath analyses from 64 patients attending a clinical practice were evaluated and the results were compared to their self-reporting status. Univariate analysis shows that all compounds evaluated gave significant differences (p < .001). Receiver operating characteristic (ROC) curves suggest that xylenes and toluene are not able to accurately determine smoking status, and benzene and the T/B ratio present potential utility in certain conditions. The highest discriminant capacity was obtained for 2,5-dimethylfuran (AUC = 0.982, 95% confidence interval [CI]: 0.969-0.995), with a cut-off value of 0.016 ppbv (sensibility = 0.965, specificity = 0.896). Drinking coffee was the only confounding parameter that can give low breath levels for this compound. The evaluation of the results obtained from the patients attending a clinical practice showed that 8% of people who claim to be nonsmokers hid their real smoking status. The results obtained confirm that the determination of 2,5-dimethylfuran in breath samples is a good and simpler alternative to conventional blood or urine tests for assessing smoking status. Analysis of 2,5-dimethylfuran in breath samples results in a simple and fast method for the determination of the smoking status of a person. This methodology presents multiple advantages as it is neither invasive nor embarrassing for patients attending clinical practices. Moreover, analysis of biomarkers in breath samples is simpler and faster than using conventional methods based on urine or blood analysis.

Validation of Veterans Affairs Electronic Medical Record Smoking Data Among Iraq- and Afghanistan-Era Veterans.

Research using the Veterans Health Administration (VA) electronic medical records (EMR) has been limited by a lack of reliable smoking data. To evaluate the validity of using VA EMR "Health Factors" data to determine smoking status among veterans with recent military service. Sensitivity, specificity, area under the receiver-operating curve (AUC), and kappa statistics were used to evaluate concordance between VA EMR smoking status and criterion smoking status. Veterans (N=2025) with service during the wars in Iraq/Afghanistan who participated in the VA Mid-Atlantic Post-Deployment Mental Health (PDMH) Study. Criterion smoking status was based on self-report during a confidential study visit. VA EMR smoking status was measured by coding health factors data entries (populated during automated clinical reminders) in three ways: based on the most common health factor, the most recent health factor, and the health factor within 12months of the criterion smoking status data collection date. Concordance with PDMH smoking status (current, former, never) was highest when determined by the most commonly observed VA EMR health factor (κ=0.69) and was not significantly impacted by psychiatric status. Agreement was higher when smoking status was dichotomized: current vs. not current (κ=0.73; sensitivity=0.84; specificity=0.91; AUC=0.87); ever vs. never (κ=0.75; sensitivity=0.85; specificity=0.90; AUC=0.87). There were substantial missing Health Factors data when restricting analyses to a 12-month period from the criterion smoking status date. Current smokers had significantly more Health Factors entries compared to never or former smokers. The use of computerized tobacco screening data to determine smoking status is valid and feasible. Results indicating that smokers have significantly more health factors entries than non-smokers suggest that caution is warranted when using the EMR to select cases for cohort studies as the risk for selection bias appears high.

Association between secondhand smoke and obesity and glucose abnormalities: data from the National Health and Nutrition Examination Survey (NHANES 1999–2010)

ObjectiveThe objective of this study is to investigate the relationship between cotinine level-confirmed secondhand smoke (SHS) exposure and glycemic parameters and obesity.Research design and methodsWe examined a cohort of 6472...

Smoking status, knowledge and attitudes towards tobacco among health profession students in Montenegro

Background Conducting educational programs in order to provide information and expand knowledge, should be primarily coordinated by health workers, which normally should be role models for a healthy lifestyle . Aim To determine smoking status, smoking related knowledge, attitudes and behavior in health profession students. Methods The Global Health Professional Student Survey (GHPSS) that collected information on health profession students (medical, nursing, dental and pharmacy) was carried out according to the established methodology of the World Health Organization. In Montenegro survey was conducted between October 2010 and March 2011 on a sample of 822 health profession students . For statistical processing of data descriptive and nonparametric analytical method as well as multivariate analysis were used. Data were analyzed using SPSS statistical software v. 17.0. Results In total 25% of surveyed students use tobacco, and 9% of them on regular basis. Health professional students that smoke more often support the idea that tobacco shouldn’t be banned in public places. They also think that health professionals do not need to be nonsmokers in order to advise their patients on the importance of non- smoking to health. In addition they don’t consider special education of health workers necessary so they could apply adequate and scientifically proven methods of smoking cessation on their patients. Conclusion Research data show that knowledge and attitudes towards tobacco among health profession students, especially regarding ban on tobacco products in public places as well as special training programs and methods of smoking cessation for health workers, are related to smoking status of respondents. Keywords: tobacco, smoking, students Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:Times New Roman; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Times New Roman; mso-bidi-theme-font:minor-bidi;} Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:Times New Roman; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Times New Roman; mso-bidi-theme-font:minor-bidi;}

Development of an algorithm for determining smoking status and behaviour over the life course from UK electronic primary care records

BackgroundPatients’ smoking status is routinely collected by General Practitioners (GP) in UK primary health care. There is an abundance of Read codes pertaining to smoking, including those relating to smoking cessation therapy, prescription, and administration codes, in addition to the more regularly employed smoking status codes. Large databases of primary care data are increasingly used for epidemiological analysis; smoking status is an important covariate in many such analyses. However, the variable definition is rarely documented in the literature.MethodsThe Secure Anonymised Information Linkage (SAIL) databank is a repository for a national collection of person-based anonymised health and socio-economic administrative data in Wales, UK. An exploration of GP smoking status data from the SAIL databank was carried out to explore the range of codes available and how they could be used in the identification of different categories of smokers, ex-smokers and never smokers. An algorithm was developed which addresses inconsistencies and changes in smoking status recording across the life course and compared with recorded smoking status as recorded in the Welsh Health Survey (WHS), 2013 and 2014 at individual level. However, the WHS could not be regarded as a “gold standard” for validation.ResultsThere were 6836 individuals in the linked dataset. Missing data were more common in GP records (6%) than in WHS (1.1%). Our algorithm assigns ex-smoker status to 34% of never-smokers, and detects 30% more smokers than are declared in the WHS data. When distinguishing between current smokers and non-smokers, the similarity between the WHS and GP data using the nearest date of comparison was κ = 0.78. When temporal conflicts had been accounted for, the similarity was κ = 0.64, showing the importance of addressing conflicts.ConclusionsWe present an algorithm for the identification of a patient’s smoking status using GP self-reported data. We have included sufficient details to allow others to replicate this work, thus increasing the standards of documentation within this research area and assessment of smoking status in routine data.

Differentiation of Chronic and Aggressive Periodontitis by FTIR Spectroscopy

Without longitudinal clinical data, it is difficult to differentiate some cases of chronic periodontitis (CP) and aggressive periodontitis (AgP). Furthermore, both forms of disease are exacerbated by tobacco use. Therefore, this cross-sectional study was planned, primarily, to determine the ability of Fourier-transform infrared (FTIR) spectroscopy to distinguish CP and AgP patients by analysis of human saliva samples and, secondarily, to assess the potential confounding influence of smoking on discriminating disease-specific spectral signatures. FTIR spectra were collected from patients with a clinical diagnosis of CP (n = 18; 7 smokers) or AgP (n = 23; 9 smokers). Self-reported smoking status, which may be unreliable, was confirmed by salivary cotinine analysis. Spectral band area analysis and hierarchical cluster analyses were performed to clarify if the 2 periodontitis groups as well as smoker and nonsmoker patients could be differentiated from each other. Significant variations in lipid, amino acid, lactic acid, and nucleic acid content were found between nonsmoker CP and AgP groups. Although significantly lower lipid, phospholipid, protein, amino acid, lactic acid, and nucleic acid content was noted in the smoker AgP group compared with the nonsmoker AgP group, in the CP group, phospholipid, protein, amino acid, and lactic acid content was significantly lower for smokers compared with the nonsmokers. Based on these variations, nonsmoker CP and AgP patients were discriminated from each other with high sensitivity and specificity. Successful differentiation was also obtained for the smoker CP and AgP groups. Thiocyanate levels successfully differentiated smokers from nonsmokers, irrespective of periodontal status, with 100% accuracy. Differentiation of AgP and CP forms, concomitant with determination of smoking status, may allow the dental health professional to tailor treatment accordingly.